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Related Experiment Video

Updated: Sep 30, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Improved Machine Learning Method for Intracranial Tumor Detection with Accelerated Particle Swarm Optimization.

K R Pradeep¹, Syam Machinathu Parambil Gangadharan², Wesam Atef Hatamleh³

¹Department of Computer Science & Engineering, B.M.S Institute of Technology and Management, Avalahalli, Bengaluru 560064, India.

Journal of Healthcare Engineering

|March 14, 2022

Summary

This summary is machine-generated.

This study introduces an automated brain tumor classification system using improved enhanced fuzzy c-means (IEnFCM) for segmentation and an accelerated particle swarm optimization-based artificial neural network model (ANNM) for accurate identification from MR images.

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Area of Science:

Medical Image Analysis
Artificial Intelligence in Radiology
Biomedical Engineering

Background:

Manual tumor identification and segmentation in MRI scans are time-consuming and complex for radiologists.
Existing image processing techniques for tumor removal from MR images require significant manual effort.
The need for automated, accurate, and efficient methods for brain tumor detection and classification is critical.

Purpose of the Study:

To enhance the performance and reduce the complexity of image segmentation for brain tumor analysis.
To develop an automated system for accurate identification and classification of brain tumors from MR images.
To improve the accuracy and quality of neural network classifiers through effective feature extraction and optimized model parameters.

Main Methods:

Image segmentation was performed using the improved enhanced fuzzy c-means (IEnFCM) method.
Feature extraction was conducted using the gray level co-occurrence matrix (GLCM) from segmented MR images.
An accelerated particle swarm optimization (APSO) algorithm was employed to train and optimize an artificial neural network model (ANNM) for tumor classification.

Main Results:

The proposed APSO-based ANNM model demonstrated automated identification and categorization of brain tumors.
The IEnFCM method improved segmentation efficiency, and GLCM provided relevant features for classification.
The study presented a resilient classification model for distinguishing between benign and malignant brain tumors.

Conclusions:

The integrated approach of IEnFCM segmentation, GLCM feature extraction, and APSO-optimized ANNM offers an effective solution for automated brain tumor classification.
This automated system can significantly alleviate the workload of radiologists in analyzing MR images for brain cancer detection.
The proposed method shows promise for improving diagnostic accuracy and efficiency in neuro-oncology.